Freezing Of Gait Episodes Research Articles

Directional Subthalamic Deep Brain Stimulation Better Improves Gait and Balance Disorders in Parkinson's Disease Patients: A Randomized Controlled Study.

To investigate the effects of directional subthalamic deep brain stimulation (STN-dDBS) on gait and balance disorders, including freezing of gait (FOG), in patients with advanced Parkinson's disease (PD). We included 10 participants who underwent STN-DBS and presented severe preoperative FOG, in a randomized, double-blind, crossover study. We used segmented DBS electrodes to investigate whether directing the predicted volume of tissue activated (VTA) to overlap the central STN preferentially improved gait and balance disorders compared to directional DBS applied in the more posterior STN (sensorimotor). We also assessed non-directional (ring-mode) STN-DBS. Our primary outcome was gait and balance control measured using instrumented gait recordings. Each patient had a pre-operative structural and diffusion-weighted imaging to model individual VTAs and to examine cortico-subthalamic connectivity. We used linear mixed-effects models to contrast the effects of central STN-dDBS, posterior STN-dDBS, and ring-mode STN-DBS. Central STN-dDBS produced significantly better improvement in gait and balance control compared to posterior STN-dDBS (p = 0.027), with fewer FOG episodes (p < 0.001). Conversely, ring-mode STN-DBS resulted in worsened postural control compared to central STN-dDBS (p = 0.009). The cortico-subthalamic connectivity with the STN VTAs involved mostly primary sensorimotor, premotor, and medial frontal cortices, with a higher overall cortico-STN connectivity with ring-mode STN-DBS. Central STN-dDBS represents the best option to improve gait and balance disorders in PD patients, including FOG. Our findings raise the possibility of reprogramming STN-DBS toward the central area in selected patients with disabling FOG and/or postural instability after surgery. ANN NEUROL 2024.

Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device.

Freezing of Gait (FOG) is prevalent in people with Parkinson's disease (PD) and severely disrupts mobility. Detecting the exact boundaries of FOG episodes may facilitate new technologies in "breaking" FOG in real-time. This study investigates the performance of automatic device-based FOG detection. Eight machine-learning classifiers (including Neural Networks, Ensemble methods, and Support Vector Machines) were developed using (i) accelerometer and (ii) combined accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed mobility tasks designed to elicit FOG. Two clinicians independently annotated exact FOG episodes using synchronized video and a flowchart algorithm based on international guidelines. Device-detected FOG episodes were compared to annotated episodes using 10-fold cross-validation and Interclass Correlation Coefficients (ICC) for agreement. Development used 50,962 windows of data and annotated activities (>10 hours). Strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity, and ICC1,1 = 0.97 for total FOG duration). Device performance varied by method, complexity, and cost matrix. The Neural Network using 67 accelerometer features achieved high sensitivity to FOG (89% sensitivity, 81% specificity, and ICC1,1 = 0.83) and stability (validation loss 5%). The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The strong clinician agreement indicates room for improvement in future device-based FOG detection. This study may enhance PD care by reducing reliance on visual FOG inspection, demonstrating that high sensitivity in automatic FOG detection is achievable.

Vibrotactile Foot Device for Freezing of Gait in Parkinson's Disease: A Pilot Study.

Vibrotactile stimulation has been studied in its efficacy of reducing freezing of gait (FOG) in patients with Parkinson's disease (PD). However, the results are still controversial. We evaluated the efficacy of a newly developed vibrotactile foot device on freezing severity and gait measures in PD patients with FOG. To evaluate the efficacy of vibrotactile foot device on PD patients with FOG. Thirty-three PD patients with FOG were examined during their "off" medication state. The efficacy of the vibrotactile foot device was evaluated using a gait protocol comprising walking trials with vibrotactile stimulation "off" and "on." Walking trials were videotaped for the offline rating by two movement disorder specialists. The Opal inertial sensor unit (128 Hz; Mobility Lab; APDM Inc., Portland, OR, USA) was used for quantitative gait analysis. The results demonstrated 33.1% reduction in number of FOG episodes (P < 0.001) and 32.6% reduction of freezing episodes (P < 0.001). Quantitative gait analysis showed a significant increase in step length (P = 0.033). A moderate negative correlation was observed between the change of percent time frozen and age (r = -0.415, P = 0.016). 73% of participants reported minimal to substantial improvement in walking with this vibrating stimulation delivered by the vibrotactile foot device. The vibrotactile foot device is an efficient device that could significantly reduce freezing severity and provide gait regulation to patients with PD experiencing frequent freezing. It could potentially be used in the home environment for improving the quality of life.

A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects

Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson’s disease. During a FOG episode, patients report that their feet are suddenly and inexplicably “glued” to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.

A novel variational mode decomposition based convolutional neural network for the identification of freezing of gait intervals for patients with Parkinson's disease

Freezing of gait (FoG) is a debilitating and serious motor system complication of Parkinson's disease (PD) that may expose patients to frequent falls and life-threating injuries. Several artificial and machine learning methods have been proposed for the prediction of FoG based upon a limited time-duration of sensory data, However, most of the related work has been insufficiently trained and tested on smaller datasets compromising the generalizability of the models. Further, the proposed models provided a prediction at a lower rate (e.g., every 7.8 s). In response to the above shortcomings, we propose a novel variational mode decomposition (VMD) based deep learning that is capable of efficiently inferring the occurrence of FoG at a higher time-resolution (i.e., every sampling period of 7.8 ms) and with a subject-independent accuracy up to 98.8 % outperforming the state-of-the-art architectures and the standard LSTM models. The proposed model will enable the prompt detection of FoG episodes and support PD sufferers reducing the likelihood of falls.

On-Demand Cueing for Freezing of Gait in Parkinson's Disease: A Randomized Controlled Trial.

Cueing can alleviate freezing of gait (FOG) in people with Parkinson's disease (PD), but using the same cues continuously in daily life may compromise effectiveness. Therefore, we developed the DeFOG-system to deliver personalized auditory cues on detection of a FOG episode. We aimed to evaluate the effects of DeFOG during a FOG-provoking protocol: (1) after 4 weeks of DeFOG-use in daily life against an active control group; (2) after immediate DeFOG-use (within-group) in different medication states. In this randomized controlled trial, 63 people with PD and daily FOG were allocated to the DeFOG or active control group. Both groups received feedback on their daily living step counts using the device, but the DeFOG group also received on-demand cueing. Video-rated FOG severity was compared pre- and post-intervention through a FOG-provoking protocol administered at home off and on-medication, but without using DeFOG. Within-group effects were tested by comparing FOG during the protocol with and without DeFOG. DeFOG-use during the 4 weeks was similar between groups, but we found no between-group differences in FOG-severity. However, the within-group analysis showed that FOG was alleviated by DeFOG (effect size d = 0.57), regardless of medication state. Combining DeFOG and medication yielded an effect size of d = 0.67. DeFOG reduced FOG considerably in a population of severe freezers both off and on medication. Nonetheless, 4 weeks of DeFOG-use in daily life did not ameliorate FOG during the protocol unless DeFOG was worn. These findings suggest that on-demand cueing is only effective when used, similar to other walking aids. © 2024 International Parkinson and Movement Disorder Society.

Freezing of gait in idiopathic normal pressure hydrocephalus

BackgroundReports of freezing of gait (FoG) in idiopathic normal pressure hydrocephalus (iNPH) are few and results are variable. This study’s objective was to evaluate the frequency of FoG in a large cohort of iNPH patients, identify FoG-associated factors, and assess FoG’s responsiveness to shunt surgery.MethodsVideotaped standardized gait protocols with iNPH patients pre- and post-shunt surgery (n = 139; median age 75 (71–79) years; 48 women) were evaluated for FoG episodes by two observers (Cohens kappa = 0.9, p < 0.001). FoG episodes were categorized. Mini-mental state examination (MMSE) and MRI white matter hyperintensities (WMH) assessment using the Fazekas scale were performed. CSF was analyzed for Beta-amyloid, Tau, and Phospho-tau. Patients with and without FoG were compared.ResultsTwenty-two patients (16%) displayed FoG at baseline, decreasing to seven (8%) after CSF shunt surgery (p = 0.039). The symptom was most frequently exhibited during turning (n = 16, 73%). Patients displaying FoG were older (77.5 vs. 74.6 years; p = 0.029), had a slower walking speed (0.59 vs. 0.89 m/s; p < 0.001), a lower Tinetti POMA score (6.8 vs. 10.8; p < 0.001), lower MMSE score (21.3 vs. 24.0; p = 0.031), and longer disease duration (4.2 vs. 2.3 years; p < 0.001) compared to patients not displaying FoG. WMH or CSF biomarkers did not differ between the groups.ConclusionsFoG is occurring frequently in iNPH patients and may be considered a typical feature of iNPH. FoG in iNPH was associated with higher age, longer disease duration, worse cognitive function, and a more unstable gait. Shunt surgery seems to improve the symptom.

Residual effects of combined vibratory and plantar stimulation while seated influences plantar pressure and spatiotemporal gait measures in individuals with Parkinson's disease exhibiting freezing of gait.

Combined plantar pressure and vibratory stimulation has been shown to decrease freezing of gait (FOG) episodes and improve spatiotemporal gait parameters compared to single stimulation in Parkinson's disease (PD) patients with FOG. However, the effect of combined plantar stimulations on plantar pressure analysis has never been explored. Forty PD patients with frequent FOG were allocated to either FOG shoes embedded with a 100 Hz vibratory stimulation at the Achilles tendons and a soft thickened silicone pad at the hallux and sole, or sham shoes with a non-working vibratory motor and a flat non-pressure silicone pad (20 patients per arm) while seated for 96 s. The objective gait and plantar pressure analysis were measured immediately after the stimulation. Outcomes included the normalized percentage of changes in percent FOG (%FOG) and plantar pressure in the heel-strike and push-off phase that were compared between pre- and post-stimulations. The FOG shoes group showed significantly decreased %FOG (81.5 ± 28.9% vs. 6.8 ± 22.1%, p < 0.001), plantar pressure in the heel-strike (47.8 ± 43.7% vs. 4.3 ± 9.8%, p < 0.001), plantar pressure in the push-off (57.7 ± 59.6% vs. 6.2 ± 11.6%, p < 0.001), force time integral (FTI) (40.9 ± 32.5% vs. 6.6 ± 17.3%, p < 0.001), and decreased heel contact time (19.3 ± 12.3% vs. 22.7 ± 32.5%, p < 0.001) when compared to the sham group. There was a strong negative correlation between %FOG and peak plantar pressure (r = -0.440, p = 0.005), plantar pressure in the heel-strike (r = -0.847, p < 0.001). Our study demonstrated that the FOG shoe could decrease FOG episodes by improving the heel-strike pressure, toe push-off and normalized heel-to-toe plantar pressure, suggesting that modification inputs from the peripheral sensory systems might significant improvement in FOG in PD.

Retrograde procedural memory is impaired in people with Parkinson's disease with freezing of gait.

Freezing of gait (FOG), is associated with impairment of different cognitive functions. Previous studies hypothesized that FOG may be due to a loss of automaticity. To explore whether FOG is associated with impairment in cognitive functions, focusing on retrograde procedural memory, the memory responsible for the automatic, implicit stored procedures that have been acquired in earlier life stages. In this cross-sectional, case-control study, 288 people with typical Parkinson's disease (PD) from the Luxembourg Parkinson's Study were assigned to Freezers (FOG+) and non-Freezers (FOG-) based on the MDS-UPDRS 2.13 (self-reported FOG episodes) and 3.11 (FOG evaluated by clinicians during gait assessment). Both groups were matched on age, sex and disease duration. Global cognition (MoCA), retrograde procedural memory and visuo-constructive abilities (CUPRO), psychomotor speed and mental flexibility (TMT) were assessed. Furthermore, we repeated our analyses by additionally controlling for depression (BDI-I). Besides lower global cognition (MoCA; p = 0.007) and mental flexibility (TMT-B and Delta-TMT; p < 0.001), FOG+ showed a lower performance in retrograde procedural memory (CUPRO-IS1; p < 0.001) compared to FOG-. After controlling additionally for depression, our main outcome variable CUPRO-IS1 remained significantly lower in FOG+ (p = 0.010). Our findings demonstrated that besides lower global cognition and mental flexibility scores, FOG+ showed lower performance in retrograde procedural memory compared to matched FOG-control patients, even when accounting for factors such as age, sex, disease duration or depression. In the context of limited treatment options, especially for non-invasive therapeutic approaches, these insights on procedural memory and FOG may lead to new hypotheses on FOG etiology and consequently the development of new treatment options.

On-Demand Gait-Synchronous Electrical Cueing in Parkinson's Disease Using Machine Learning and Edge Computing: A Pilot Study.

Goal: Parkinson's disease (PD) can lead to gait impairment and Freezing of Gait (FoG). Recent advances in cueing technologies have enhanced mobility in PD patients. While sensor technology and machine learning offer real-time detection for on-demand cueing, existing systems are limited by the usage of smartphones between the sensor(s) and cueing device(s) for data processing. By avoiding this we aim at improving usability, robustness, and detection delay. Methods: We present a new technical solution, that runs detection and cueing algorithms directly on the sensing and cueing devices, bypassing the smartphone. This solution relies on edge computing on the devices' hardware. The wearable system consists of a single inertial sensor to control a stimulator and enables machine-learning-based FoG detection by classifying foot motion phases as either normal or FoG-affected. We demonstrate the system's functionality and safety during on-demand gait-synchronous electrical cueing in two patients, performing freezing of gait assessments. As references, motion phases and FoG episodes have been video-annotated. Results: The analysis confirms adequate gait phase and FoG detection performance. The mobility assistant detected foot motions with a rate above 94 % and classified them with an accuracy of 84 % into normal or FoG-affected. The FoG detection delay is mainly defined by the foot-motion duration, which is below the delay in existing sliding-window approaches. Conclusions: Direct computing on the sensor and cueing devices ensures robust detection of FoG-affected motions for on demand cueing synchronized with the gait. The proposed solution can be easily adopted to other sensor and cueing modalities.

Detection of freezing of gait in Parkinson's disease from foot-pressure sensing insoles using a temporal convolutional neural network.

Freezing of gait (FoG) is a common and debilitating symptom of Parkinson's disease (PD) that can lead to falls and reduced quality of life. Wearable sensors have been used to detect FoG, but current methods have limitations in accuracy and practicality. In this paper, we aimed to develop a deep learning model using pressure sensor data from wearable insoles to accurately detect FoG in PD patients. We recruited 14 PD patients and collected data from multiple trials of a standardized walking test using the Pedar insole system. We proposed temporal convolutional neural network (TCNN) and applied rigorous data filtering and selective participant inclusion criteria to ensure the integrity of the dataset. We mapped the sensor data to a structured matrix and normalized it for input into our TCNN. We used a train-test split to evaluate the performance of the model. We found that TCNN model achieved the highest accuracy, precision, sensitivity, specificity, and F1 score for FoG detection compared to other models. The TCNN model also showed good performance in detecting FoG episodes, even in various types of sensor noise situations. We demonstrated the potential of using wearable pressure sensors and machine learning models for FoG detection in PD patients. The TCNN model showed promising results and could be used in future studies to develop a real-time FoG detection system to improve PD patients' safety and quality of life. Additionally, our noise impact analysis identifies critical sensor locations, suggesting potential for reducing sensor numbers.

Triggers for freezing of gait in individuals with Parkinson's disease: a systematic review.

Freezing of Gait (FOG) is a motor symptom frequently observed in advanced Parkinson's disease. However, due to its paroxysmal nature and diverse presentation, assessing FOG in a clinical setting can be challenging. Before FOG can be fully investigated, it is critical that a reliable experimental setting is established in which FOG can be evoked in a standardized manner, but the efficacy of various gait tasks and triggers for eliciting FOG remains unclear. This study aimed to conduct a systematic review of the existing literature and evaluate the available evidence for the relationship between specific motor tasks, triggers, and FOG episodes in individuals with Parkinson's disease (PwPD). We conducted a literature search on four online databases (PubMed, Web of Science, EMBASE, and Cochrane Library) using the keywords "Parkinson's disease," "Freezing of Gait", "triggers" and "tasks". A total of 128 articles met the inclusion criteria and were included in our analysis. The review found that a wide range of gait tasks were employed in studies assessing FOG among PD patients. However, three tasks (turning, dual tasking, and straight walking) emerged as the most frequently used. Turning (28%) appears to be the most effective trigger for eliciting FOG in PwPD, followed by walking through a doorway (14%) and dual tasking (10%). This review thereby supports the utilisation of turning, especially a 360-degree turn, as a reliable trigger for FOG in PwPD. This finding could be beneficial to clinicians conducting clinical evaluations and researchers aiming to assess FOG in a laboratory environment.

Upregulation of the parietal cortex improves freezing of gait in Parkinson's disease

BACKGROUND The posterior parietal cortex (PPC) is a key brain area for visuospatial processing and locomotion. It has been repetitively shown to be involved in the neural correlates of freezing of gait (FOG), a common symptom of Parkinson's disease (PD). However, current neuroimaging modalities do not allow to precisely determine the role of the PPC during real FOG episodes. OBJECTIVES The purpose of this study was to modulate the PPC cortical excitability using repetitive transcranial magnetic stimulation (rTMS) to determine whether the PPC contributes to FOG or compensates for dysfunctional neural networks to reduce FOG. METHODS Fourteen participants with PD who experience freezing took part in a proof of principle study consisting of three experimental sessions targeting the PPC with inhibitory, excitatory, and sham rTMS. Objective FOG outcomes and cortical excitability measurements were acquired before and after each stimulation protocol. RESULTS Increasing PPC excitability resulted in significantly fewer freezing episodes and percent time frozen during a FOG-provoking task. This reduction in FOG most likely emerged from the trend in PPC inhibiting the lower leg motor cortex excitability. CONCLUSION Our results suggest that the recruitment of the PPC is linked to less FOG, providing support for the beneficial role of the PPC upregulation in preventing FOG. This could potentially be linked to a reduction of the cortical input burden on the basal ganglia prior to FOG. Excitatory rTMS interventions targeting the PPC may have the potential to reduce FOG.

Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease.

Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.

Good vibrations: tactile cueing for freezing of gait in Parkinson’s disease

BackgroundCueing strategies can alleviate freezing of gait (FOG) in people with Parkinson’s disease (PD). We evaluated tactile cueing delivered via vibrating socks, which has the benefit of not being noticeable to bystanders.ObjectiveTo evaluate the effect of tactile cueing compared to auditory cueing on FOG.MethodsThirty-one persons with PD with FOG performed gait tasks during both ON and OFF state. The effect of open loop and closed loop tactile cueing, as delivered by vibrating socks, was compared to an active control group (auditory cueing) and to a baseline condition (uncued gait). These four conditions were balanced between subjects. Gait tasks were videotaped and annotated for FOG by two experienced raters. Motion data were collected to analyze spatiotemporal gait parameters. Responders were defined as manifesting a relative reduction of > 10% in the percent time frozen compared to uncued gait.ResultsThe average percent time frozen during uncued gait was 11.2% in ON and 21.5% in OFF state. None of the three tested cueing modalities affected the percentage of time frozen in either the ON (p = 0.20) or OFF state (p = 0.12). The number of FOG episodes and spatiotemporal gait parameters were also not affected. We found that 22 out of 31 subjects responded to cueing, the response to the three types of cueing was highly individual.ConclusionsCueing did not improve FOG at the group level; however, tactile as well as auditory cueing improved FOG in many individuals. This highlights the need for a personalized approach when using cueing to treat FOG.

A Novel Physical Mobility Task to Assess Freezers in Parkinson's Disease.

Freezing of gait (FOG), one of the most disabling features of Parkinson's disease (PD), is a brief episodic absence or marked reduction in stride progression despite the intention to walk. Progressively more people who experience FOG restrict their walking and reduce their level of physical activity. The purpose of this study is to develop and validate a physical mobility task that induces freezing of gait in a controlled environment, employing known triggers of FOG episodes according to the literature. To validate the physical mobility tasks, we recruited 10 volunteers that suffered PD-associated freezing (60.6 ± 7.29 years-old) with new FOG-Q ranging from 12 to 26. The validation of the proposed method was carried out using inertial sensors and video recordings. All subjects were assessed during the OFF and ON medication states. The total number of FOG occurrences during data collection was 144. The proposed tasks were able to trigger 120 FOG episodes, while the TUG test caused 24. The Inertial Measurement Unit (IMU) with accelerometer and gyroscope could not only detect FOG episodes but also allowed us to visualize the three types of FOG: akinesia, festination and trembling in place.

Evaluation of Gait Parameters using Wearable Sensors in Simulated Freezing of Gait Episodes

Background and Hypothesis: Freezing of gait (FoG), the failure to initiate or maintain locomotion, is a common symptom in Parkinsonism that severely impacts patients’ quality of life. Wearable technology such as inertial measurement units (IMU) are being widely investigated as a method of detecting these episodes with increasing sensitivity and specificity. The aim of this initial study is to collect objective gait data using IMU sensors via Perception Neuron software and to develop an analysis pipeline for quick in-clinic calculation of gait parameters.&#x0D; Experimental Design: 16 IMU’s were attached to the feet, legs, arms, waist, back, and head in individuals without any gait abnormalities. Subjects were instructed to walk 10 meters normally, followed by 10 meters with interspersed, simulated FoG episodes. This data was processed and analyzed through Axis Studio and MATLAB to identify kinematic markers associated with gait. The correlation between the right and left foot gyroscopic data in the z-axis was determined and plotted against time, with any correlation less than 0.5 being considered a FoG episode. Further, parameters of gait including stride duration, stance duration, swing duration, and stride length were calculated from input of the raw data to establish a control comparison for future studies with Parkinson’s patients.&#x0D; Results: Data analysis demonstrated increased stride duration, stance duration and swing duration with shorter stride length in the stimulated FoG episodes as compared to normal gait. The developed MATLAB code was further able to accurately distinguish the simulated FoG episodes from normal gait given the correlation parameter difference of &lt;0.5.&#x0D; Conclusion: Our findings showed that data obtained from Perception Neuron IMU can be processed to succinctly detect FoG episodes and calculate gait parameters. Ultimately, this data will be used as the baseline for a future study investigating the use of spinal cord stimulation as treatment for FoG in individuals with Parkinson’s Disease.

Techniques for the detection and management of freezing of gait in Parkinson's disease – A systematic review and future perspectives

Freezing of Gait (FoG) is one of the most critical debilitating motor symptoms of advanced Parkinson's disease (PD) with a higher rate of occurrence in aged people. PD affects the cardinal motor functioning and leads to non-motor symptoms, including cognitive and neurobehavioral abnormalities, autonomic dysfunctions and sleep disorders. Since its pathogenesis is complex and unclear yet, this paper targets the studies done on the pathophysiology and epidemiology of FoG in PD. Gait disorder and cardinal features vary from festination (involuntary hurrying in walking) to freezing of gait (breakdown of repetitive movement of steps despite the intention to walk) in patients. Hence, it is difficult to assess the FoG in clinical trials. Therefore, the current research emphasizes wearable sensor-based systems over pharmacology and surgical methods.•This paper presents a technological review of various techniques used for the assessment of FoG with a comprehensive comparison.•Researchers are aiming at the development of wireless sensor-based assistive devices to (a) predict the FoG episode in a different environment, (b) acquire the long-term data for real-time analysis, and (c) cue the FoG patients.•We summarize the work done till now and future research directions needed for a suitable cueing mechanism to overcome FoG.

Predicting the Onset of Freezing of Gait Using EEG Dynamics

Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping.

Technological support for people with Parkinson’s disease: a narrative review

Background. Parkinson’s disease resulting from the degeneration of specific areas of the brain, can cause severely disabling symptoms, such as freezing of gait. Freezing of gait increases the risk of falls through worsening of physical mobility, muscle stiffening, slow uncoordinated movements and often leads to hospitalization with significant worsening of quality of life for such patients. Indeed, older patients are at a significantly higher risk of negative outcomes related to PD. Objective. This work focuses on the most recent findings regarding non-invasive intervention and monitoring strategies to counteract the effects of freezing of gait. In addition, several devices can also provide support for diagnosis, treatment, and quality of daily life, especially in older patients with PD. Methods. This narrative review describes the current state of the art of devices based on cueing, monitoring and rehabilitation systems. Fifty- seven studies were selected. Results. Overall findings demonstrates that these smart devices can act as a valid aid tools able to: i) learn patient motor habits in order to intervene during a freezing of gait episode, ii) monitor daily conditions, iii) send and store data on disease progression, iv) provide useful information for rehabilitation programs in a clinical or home care environment. Conclusions. These technologies hold excellent prospects for patient treatment tailoring, especially in older patients in home care.